





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_           =	logging.getLogger()
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]						)  ->  Any:






   """simple docstring"""







   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   os.path.join(SCREAMING_SNAKE_CASE_				,	'''all_results.json'''						)
   if os.path.exists(SCREAMING_SNAKE_CASE_						):
      with open(SCREAMING_SNAKE_CASE_				,	'''r'''						) as f:
         _UpperCAmelCase           =   json.load(SCREAMING_SNAKE_CASE_						)
   else:
      raise ValueError(F'''can\'t find {path}'''						)
   return results


UpperCAmelCase_           =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




@require_torch_tpu
class 			__UpperCamelCase    (							A__    ):





     def 		UpperCamelCase(     self					):
        import xla_spawn

        _UpperCAmelCase           =   self.get_auto_remove_tmp_dir()
        _UpperCAmelCase           =   f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

        with patch.object(_UpperCamelCase				,							'''argv'''				,							_UpperCamelCase					):
           _UpperCAmelCase           =   time()
           xla_spawn.main()
           _UpperCAmelCase           =   time()

           _UpperCAmelCase           =   get_results(_UpperCamelCase					)
           self.assertGreaterEqual(result['''eval_accuracy''']				,							0.75					)

           # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
           self.assertLess(end - start				,							500					)





     def 		UpperCamelCase(     self					):
        import xla_spawn

        _UpperCAmelCase           =   '''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
        with patch.object(_UpperCamelCase				,							'''argv'''				,							_UpperCamelCase					):
           xla_spawn.main()



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)
						# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
						parser.add_argument(
						    '--original_config_file',
						    default=None,
						    type=str,
						    help='The YAML config file corresponding to the original architecture.',
						)
						parser.add_argument(
						    '--num_in_channels',
						    default=None,
						    type=int,
						    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
						)
						parser.add_argument(
						    '--scheduler_type',
						    default='pndm',
						    type=str,
						    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
						)
						parser.add_argument(
						    '--pipeline_type',
						    default=None,
						    type=str,
						    help=(
						        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
						        '. If `None` pipeline will be automatically inferred.'
						    ),
						)
						parser.add_argument(
						    '--image_size',
						    default=None,
						    type=int,
						    help=(
						        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
						        ' Base. Use 768 for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--prediction_type',
						    default=None,
						    type=str,
						    help=(
						        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
						        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--extract_ema',
						    action='store_true',
						    help=(
						        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
						        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
						        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
						    ),
						)
						parser.add_argument(
						    '--upcast_attention',
						    action='store_true',
						    help=(
						        'Whether the attention computation should always be upcasted. This is necessary when running stable'
						        ' diffusion 2.1.'
						    ),
						)
						parser.add_argument(
						    '--from_safetensors',
						    action='store_true',
						    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
						)
						parser.add_argument(
						    '--to_safetensors',
						    action='store_true',
						    help='Whether to store pipeline in safetensors format or not.',
						)
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
						parser.add_argument(
						    '--stable_unclip',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
						)
						parser.add_argument(
						    '--stable_unclip_prior',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
						)
						parser.add_argument(
						    '--clip_stats_path',
						    type=str,
						    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
						    required=False,
						)
						parser.add_argument(
						    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
						)
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--vae_path',
						    type=str,
						    default=None,
						    required=False,
						    help='Set to a path, hub id to an already converted vae to not convert it again.',
						)
						__UpperCamelCase     		=							parser.parse_args()

						__UpperCamelCase     		=							download_from_original_stable_diffusion_ckpt(
						    checkpoint_path=args.checkpoint_path,
						    original_config_file=args.original_config_file,
						    image_size=args.image_size,
						    prediction_type=args.prediction_type,
						    model_type=args.pipeline_type,
						    extract_ema=args.extract_ema,
						    scheduler_type=args.scheduler_type,
						    num_in_channels=args.num_in_channels,
						    upcast_attention=args.upcast_attention,
						    from_safetensors=args.from_safetensors,
						    device=args.device,
						    stable_unclip=args.stable_unclip,
						    stable_unclip_prior=args.stable_unclip_prior,
						    clip_stats_path=args.clip_stats_path,
						    controlnet=args.controlnet,
						    vae_path=args.vae_path,
						)

						if args.half:
												pipe.to(torch_dtype=torch.floataa)

						if args.controlnet:
												# only save the controlnet model
												pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
						else:
												pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


